Google BigQuery features and reviews of 2020
Google BigQuery helps organizations, data analysts, and data managers analyze big data, get real-time insight, and make informed business decisions.
Google BigQuery is a serverless, cloud-based, and highly scalable data warehouse software designed to help small, medium, and big businesses improve business agility.
This software allows organizations to gain valuable insights with predictive and real-time analytics. Data analysts and managers can easily store and query streaming data in real-time, get current information about operations and business processes.
The machine capabilities of this software allow companies to forecast and predict business outcomes without the need to export data. With BigQuery ML, data analysts and data scientists can build and execute machine learning models on semi-structured or structured data in BigQuery quickly using standard SQL queries. Users can export BigQuery machine learning models for forecasts or online predictions into their serving layer or cloud artificial intelligence (AI) platforms.
Google BigQuery empowers companies to access data and share insights with employees, remote teams, and third-parties. With a few clicks, users can seamlessly build dashboards and generate reports using powerful business intelligence tools and applications.
With Google BigQuery BI Engine, data analysts and administrators can mine massive and complex datasets efficiently with high concurrency and agile query response time. BigQuery BI Engine supports seamless integration with tools that can help to speed up data exploration, mining, and analysis, including Data Studio and Looker.
Google BigQuery GIS offers support for geospatial analysis and allows users to analyze and visualize data using location standard SQL geography functions, geography data types, and location intelligence. With Google BigQuery’s serverless data warehousing, users do not need to worry about infrastructure management, upgrade, and security. Google BigQuery handles resource provisioning so that users can focus on data mining and analysis.
Google BigQuery offers robust data security, reliability controls, and governance. More so, the software allows users to protect and operate their data with 99.9 percent uptime and high availability.
Google BigQuery helps data managers to handle extensive data and analytical workloads. This software allows companies to store, run queries, load, and export big data for analysis.
Users can query and analyze data petabytes of data at fast speed using the standard ANSI SQL, ultimately reducing the need for code rewrites. Google BigQuery also offers JDBC and ODBC drivers to ensure that data applications can interact with its powerful engine.
Through federated queries and without moving data, Google BigQuery processes external data sources in transactional databases like Cloud SQL and Bigtable, cloud object storage for ORC and Parquet open-source file formats, or spreadsheets in Google Drive. Data engineers can run data science workflows directly on BigQuery using open-source tools such as TensorFlow, Spark, Apache Beam, Dataflow, MapReduce, scikit-learn, and Pandas.
Google BigQuery helps data analysts and organizations gain valuable insights with real-time and predictive analytics. BigQuery ML allows data analysts and data engineers to build and operate machine learning models on structured or semi-structured data using standard SQL quickly. With this tool, Users can export BigQuery machine learning models for forecasting into a cloud artificial intelligence platform or their big data serving layer.
Google BigQuery supports multiple machine learning models. Data analysts can use binary logistic regression for classification, linear regression for forecasting, multiclass logistic regression for classification, TensorFlow model importing, or K-means clustering for data segmentation. For example, companies can use binary logistic regression to determine whether a customer will buy a product or use K-means to segment customers or markets.
Data Scientists and managers use Google BigQuery to store, query streaming data, and get real-time information on business and operational processes. Without exporting data, predict business outcomes easily, and make business decisions with in-built machine learning.
Google BigQuery makes it easy for companies, business, and data management teams to access data and share insights. With this software, users can securely share data with employees, business units, and third-parties, including customers, suppliers, and vendors.
Organizations use Google BigQuery to create beautiful dashboards and reports using creative business intelligence tools and share analytical insights with business, operations, and management teams.
With the Google BigQuery data transfer service, users can seamlessly transfer data from Amazon S3 and Teradata to BigQuery. The software automatically transfers data from external data sources, including Google Ad, YouTube, Google Marketing Platform, and third-party SaaS solutions to BigQuery on a fully managed and scheduled basis.
Google BigQuery helps data analysts and organizations accelerate data exploration, analysis, and mining. Google BigQuery BI Engine allows data analysts and administrators to mine large and complex datasets efficiently with high concurrency and quick query response time.
BigQuery BI Engine supports seamless integration with Google tools that can help to accelerate data exploration, mining, and analysis, including Data Studio and Looker. Data managers can use Data Studio to build secure, scalable, and interactive dashboards and reports that mirror business performance.
Google BigQuery allows businesses to unlock business potential by analyzing and visualizing geospatial data. BigQuery GIS enables enterprises to explore the possibilities of geospatial data analysis. The software combines the power of Google BigQuery’s serverless architecture with native support for geospatial analysis so that businesses can enhance their analytical workflows with location-based intelligence.
Google BigQuery data warehouse software helps users to streamline geospatial analysis, explore spatial data with a different approach, and uncover new and potential lines of businesses.
BigQuery GIS offers SQL support lines, arbitrary points, polygons, and multi-polygons in geospatial formats such as GeoJSON and WKT. Businesses use this type of location-based data to ascertain when a parcel will arrive or to determine customers that should receive emails from a particular business unit or location.
Google BigQuery helps data analysts drive productivity by automating critical server management tasks.
With Google BigQuery serverless data warehousing, organizations can focus on mining data to drive business success. Google handles resource provisioning and server management tasks, including managing, upgrading, and securing the infrastructure.
Also, Google BigQuery automatically backs up, replicates data, and maintains backup history so that organizations can compare different data versions, restore valuable data and achieve business continuity. This software encrypts data by default whether the content is at rest or in transit. Organizations rely on Google BigQuery’s strong data governance, governance, and reliability controls to achieve 99 percent uptime and high availability.
Google BigQuery helps data engineers and companies query data and get real-time updates about critical business processes. With this software, managers can harness business intelligence tools to build reports and dashboards and share analytical insights. Organizations use this tool to analyze big data, predict business outcomes, and enhance business agility.